Enhanced Analysis of Falling Weight Deflectometer Data for Use With Mechanistic-Empirical Flexible Pavement Design and Analysis and Recommendations for Improvements to Falling Weight Deflectometers

CHAPTER 1. INTRODUCTION

This report describes the efforts undertaken to develop methodologies for the determination of the damaged master curve and unbound material properties of in-service pavements from enhanced falling weight deflectometer (FWD) data.

BACKGROUND

Pavement characterization is important for determining cost-effective treatment type and allocation of funds and resources to maintain and rehabilitate the deteriorating highway infrastructure. The key element in the success of any pavement management system responsible for making preventive and corrective decisions is a proper assessment of the present status and an accurate prediction of the future performance of pavement structure. Characterizing pavement properties plays a critical role in both activities.

Nondestructive testing (NDT) is a well-recognized method for evaluating the structural capacity and integrity of highway and airfield pavements. The use of an FWD is one of the most frequently employed NDT methods for evaluating the structural integrity of an existing pavement. As its full name implies, the FWD is equipped with a falling mass mechanism capable of inducing an impact load on the pavement surface. Because of the nature of the impact load generated by a falling mass, the load typically has a short duration (usually 20 to 40 ms) and gives rise to a stress wave that propagates through the pavement structure. The resulting time-dependent response of the pavement structure, or more specifically, the vertical deflection at the pavement surface resulting from the stress wave, is measured at various radial distances from the load and is recorded for the structural analysis of the pavement system. FWD testing enables the use of a mechanistic approach for pavement design and rehabilitation by allowing for backcalculating in situ material properties from the measured field surface deflections through appropriate analysis techniques. In deflection methods, pavement deflections represent an overall system response of the pavement layers and the roadbed soil to an applied load. Pavement surface deflections have traditionally been used as an indicator of its structural capacity.

The need to accurately characterize the structural condition of existing pavements has increased with the recent development, release, and ongoing implementation of the Mechanistic-Empirical Pavement Design Guide (MEPDG).⁽¹⁾ A number of different material inputs are required in the procedure, and it is important that these be adequately characterized and defined. The analysis of deflection data collected by the FWD provides a fast and reliable way of characterizing the properties of the paving layers, as well as assessing the load-carrying capacity of existing pavement structures. With the release of the new MEPDG, there is a strong need for identifying and evaluating the way that FWD testing is operated and integrated in the new design procedure.⁽¹⁾

PROJECT SCOPE

The MEPDG theoretical and empirical models predict response of flexible and rigid pavements.⁽¹⁾ The dynamic modulus (|E*|) master curve of asphalt concrete (AC) layer is a fundamental material property that is required as an input in MEPDG for a flexible pavement analysis. Knowledge of the |E*| master curve of an in-service pavement using FWD data can lead to more accurate estimation of its remaining life.

The overall objective of the research was to theoretically determine a static and dynamic viscoelastic flexible pavement response model and relevant FWD data inputs to be used in a backcalculation scheme for determining damaged E(t) master curve and unbound material properties of in-service pavements.

PROJECT OBJECTIVES

The objectives of the project are as follows:

• Review the status of FWD equipment, data collection, analysis, and interpretation, including dynamic backcalculation, as they relate to the models and procedures incorporated in the MEPDG.⁽¹⁾
  
  
• Conduct theoretical analyses to identify suitable data requirements from FWD in light of current FWD technology and feasible equipment enhancements.
  
  
• Develop methodologies for the determination of the damaged master curve and unbound material properties of in-service pavements from enhanced FWD data.
  
  
• Develop recommendations for FWD equipment enhancements.

REPORT STRUCTURE

The remaining chapters of this report are organized as follows:

• Chapter 2 reviews the status of FWD equipment, data collection, analysis, and interpretation, including dynamic backcalculation, as they relate to the models and procedures incorporated in MEPDG.⁽¹⁾
  
  
• Chapter 3 presents the detailed analysis of the Long-Term Pavement Performance (LTPP) Program database conducted to assess when dynamic effects and nonlinearity are prevalent.
  
  
• Chapter 4 outlines the development of a quasi-static viscoelastic flexible pavement response model (LAVA) and a backcalculation scheme (BACKLAVA).
  
  
• Chapter 5 describes a newly developed dynamic time-domain viscoelastic flexible pavement response model (ViscoWave-II) and a backcalculation algorithm (DYNABACK-VE). It presents the verification results for the developed algorithm. Verification was accomplished by comparing the simulation results from the developed algorithm to some of the other existing solutions. Then the iteration algorithms were tested using theoretically generated deflection time histories and field measured data.
  
  
• Chapter 6 provides recommendations for FWD equipment enhancements.
  
  
• Chapter 7 summarizes the work performed under this project, outlines the main research products developed, and presents the main findings of the study.
  
  
• Appendix A presents the results of the comparison between the LAVAN and the nonlinear finite element method (FEM) software MICHPAVE. The algorithm was compared for the cases when the unbound layer was considered as a single layer for nonlinearity calculations (Algorithm1) and when the layer was divided into two sections (Algorithm2).
  
  
• Appendix B describes the detailed analysis on the effect of using multiple pulses on the backcalculation results.
  
  
• Appendix C describes in detail the theoretical development of the new algorithm for ViscoWave-II. The theoretical development for the proposed methodology follows similar steps to those used for the development of LAMDA, the spectral element method, which used the discrete transforms for solving the wave equations.⁽²⁾ However, the proposed solution uses the continuous integral transforms (namely Laplace and Hankel transforms) that are more appropriate for transient, nonperiodic signals.⁽³⁾
  
  
• Appendix D presents the FWD site/field data collected as part of this project and used for backcalculation.